Scalable Non-negative Matrix Factorization of the Human Cell Census Reveals Interpretable Transcriptional Programs

Abstract

Large single-cell atlases now span tens of millions of cells, yet few provide reusable and interpretable reference representations that support direct biological reasoning at atlas-scale. Here, we present an interpretable Non-negative Matrix Factorization reference embedding of 28.5 million healthy cells and approximately 60,000 genes from the Human Cell Census. The resulting gene and cell weights define additive transcriptional programs that align with annotated cell types and organized biological pathways. New datasets can be projected into this fixed reference space without fine-tuning or retraining, as we demonstrate using an independent cystic fibrosis dataset. This resource provides a transparent coordinate system for exploratory analysis and hypothesis generation, complementing deep embeddings that prioritize integration or prediction with a representation designed for interpretability and reuse.